1. Field
Certain aspects of the present disclosure generally relate to neural system engineering and, more particularly, to a method and apparatus for neural learning of natural multi-spike trains in spiking neural networks.
2. Background
The typical spike-timing-dependent plasticity (STDP) learning rule adapts a synaptic weight based on timing between a single pair of pre (input) spike and post (output) spike. However, natural spike trains (e.g., in biology) and spike trains in spiking neural networks do not generally have clearly separated pairs of spikes. Therefore, multiple input and output spikes represent the general case. Yet, learning based on the STDP may fail to converge due to oscillation of weight increases and decreases. Moreover, the basic STDP learning rule may fail to account for biologically observed learning in multi-spike contexts.
Two main models have been proposed in the literature based on theories. One method requires computation based on all combination pairs (pre-synaptic and post-synaptic). However, this approach is often computationally expensive. The other method explains rate-based effects, but not temporal effects that are relevant for temporal coding. Furthermore, this approach can be dependent on the neuron dynamics model and may require complex filtering on multiple time-scales.
Therefore, a computationally practical method is desired that offers stability and fast learning for spiking networks operating in a temporal coding context, which can also be biologically plausible.